"""
AI算法管理API - 简化版本用于测试
"""
from fastapi import APIRouter, HTTPException
from typing import List, Dict, Any, Optional
from datetime import datetime

router = APIRouter()

# 模拟数据 - 六大AI系统的算法模型
MOCK_MODELS = [
    # 1. 人流安全管理AI系统
    {
        "model_id": "yolov8n_person",
        "name": "YOLOv8n 人员检测",
        "version": "1.0.0",
        "type": "person_detection",
        "category": "crowd_safety",
        "status": "active",
        "accuracy": 0.85,
        "performance_metrics": {
            "fps": 30,
            "latency": 33,
            "memory_usage": 512
        },
        "description": "基于YOLOv8的高效人员检测算法，用于人流密度分析和异常聚集检测",
        "created_at": "2024-01-01T00:00:00Z",
        "updated_at": "2024-01-01T00:00:00Z"
    },
    {
        "model_id": "crowd_density_analyzer",
        "name": "人流密度分析器",
        "version": "2.1.0",
        "type": "crowd_analysis",
        "category": "crowd_safety",
        "status": "active",
        "accuracy": 0.92,
        "performance_metrics": {
            "fps": 20,
            "latency": 50,
            "memory_usage": 768
        },
        "description": "实时分析人流密度，检测异常聚集和疏散路径规划",
        "created_at": "2024-01-01T00:00:00Z",
        "updated_at": "2024-01-01T00:00:00Z"
    },
    # 2. 行为安全监测AI系统
    {
        "model_id": "pose_estimation_mediapipe",
        "name": "MediaPipe姿态估计",
        "version": "1.5.0",
        "type": "pose_detection",
        "category": "behavior_safety",
        "status": "active",
        "accuracy": 0.88,
        "performance_metrics": {
            "fps": 25,
            "latency": 40,
            "memory_usage": 640
        },
        "description": "基于MediaPipe的实时姿态估计，用于行为识别和冲突检测",
        "created_at": "2024-01-01T00:00:00Z",
        "updated_at": "2024-01-01T00:00:00Z"
    },
    {
        "model_id": "conflict_detector",
        "name": "冲突行为检测器",
        "version": "1.3.0",
        "type": "behavior_analysis",
        "category": "behavior_safety",
        "status": "active",
        "accuracy": 0.91,
        "performance_metrics": {
            "fps": 15,
            "latency": 67,
            "memory_usage": 896
        },
        "description": "智能识别打架、推搡等冲突行为，实时预警危险情况",
        "created_at": "2024-01-01T00:00:00Z",
        "updated_at": "2024-01-01T00:00:00Z"
    },
    # 3. 环境卫生管理AI系统
    {
        "model_id": "waste_detector_v2",
        "name": "垃圾检测器 V2",
        "version": "2.0.0",
        "type": "waste_detection",
        "category": "hygiene_management",
        "status": "active",
        "accuracy": 0.87,
        "performance_metrics": {
            "fps": 18,
            "latency": 55,
            "memory_usage": 720
        },
        "description": "智能识别和分类各种垃圾，支持垃圾分类和清洁度评估",
        "created_at": "2024-01-01T00:00:00Z",
        "updated_at": "2024-01-01T00:00:00Z"
    },
    {
        "model_id": "cleanliness_assessor",
        "name": "清洁度评估器",
        "version": "1.4.0",
        "type": "cleanliness_analysis",
        "category": "hygiene_management",
        "status": "active",
        "accuracy": 0.89,
        "performance_metrics": {
            "fps": 12,
            "latency": 83,
            "memory_usage": 512
        },
        "description": "基于图像分析的清洁度评分系统，量化环境卫生状况",
        "created_at": "2024-01-01T00:00:00Z",
        "updated_at": "2024-01-01T00:00:00Z"
    },
    # 4. 能耗管理AI系统
    {
        "model_id": "lighting_detector",
        "name": "照明状态检测器",
        "version": "1.2.0",
        "type": "lighting_detection",
        "category": "energy_management",
        "status": "active",
        "accuracy": 0.94,
        "performance_metrics": {
            "fps": 10,
            "latency": 100,
            "memory_usage": 384
        },
        "description": "智能检测灯光开关状态和自然光照度，优化能耗管理",
        "created_at": "2024-01-01T00:00:00Z",
        "updated_at": "2024-01-01T00:00:00Z"
    },
    {
        "model_id": "space_occupancy_detector",
        "name": "空间占用检测器",
        "version": "1.6.0",
        "type": "occupancy_detection",
        "category": "energy_management",
        "status": "active",
        "accuracy": 0.91,
        "performance_metrics": {
            "fps": 8,
            "latency": 125,
            "memory_usage": 456
        },
        "description": "检测教室、办公室等空间的占用状态，提供节能建议",
        "created_at": "2024-01-01T00:00:00Z",
        "updated_at": "2024-01-01T00:00:00Z"
    },
    # 5. 课堂质量评估AI系统
    {
        "model_id": "attention_analyzer",
        "name": "专注度分析器",
        "version": "1.8.0",
        "type": "attention_analysis",
        "category": "classroom_quality",
        "status": "active",
        "accuracy": 0.86,
        "performance_metrics": {
            "fps": 15,
            "latency": 67,
            "memory_usage": 612
        },
        "description": "基于人脸和姿态分析学生专注度，评估课堂参与情况",
        "created_at": "2024-01-01T00:00:00Z",
        "updated_at": "2024-01-01T00:00:00Z"
    },
    {
        "model_id": "participation_tracker",
        "name": "参与度跟踪器",
        "version": "1.1.0",
        "type": "participation_analysis",
        "category": "classroom_quality",
        "status": "active",
        "accuracy": 0.83,
        "performance_metrics": {
            "fps": 12,
            "latency": 83,
            "memory_usage": 548
        },
        "description": "识别举手、互动等参与行为，量化课堂活跃度",
        "created_at": "2024-01-01T00:00:00Z",
        "updated_at": "2024-01-01T00:00:00Z"
    },
    # 6. 教学质量评估AI系统
    {
        "model_id": "teacher_behavior_analyzer",
        "name": "教师行为分析器",
        "version": "1.7.0",
        "type": "teacher_analysis",
        "category": "teaching_quality",
        "status": "active",
        "accuracy": 0.88,
        "performance_metrics": {
            "fps": 10,
            "latency": 100,
            "memory_usage": 672
        },
        "description": "分析教师手势、表情、动作，评估教学行为和风格",
        "created_at": "2024-01-01T00:00:00Z",
        "updated_at": "2024-01-01T00:00:00Z"
    },
    {
        "model_id": "interaction_evaluator",
        "name": "师生互动评估器",
        "version": "1.3.0",
        "type": "interaction_analysis",
        "category": "teaching_quality",
        "status": "active",
        "accuracy": 0.85,
        "performance_metrics": {
            "fps": 8,
            "latency": 125,
            "memory_usage": 584
        },
        "description": "评估师生互动频率和质量，提供教学改进建议",
        "created_at": "2024-01-01T00:00:00Z",
        "updated_at": "2024-01-01T00:00:00Z"
    }
]

MOCK_TASKS = [
    {
        "task_id": "task_001",
        "camera_id": "camera_001",
        "task_type": "person_detection",
        "status": "running",
        "created_at": "2024-01-01T00:00:00Z",
        "updated_at": "2024-01-01T00:00:00Z",
        "config": {
            "confidence_threshold": 0.5,
            "model_id": "yolov8n_person"
        }
    },
    {
        "task_id": "task_002",
        "camera_id": "camera_002",
        "task_type": "person_detection",
        "status": "completed",
        "created_at": "2024-01-01T00:00:00Z",
        "updated_at": "2024-01-01T00:00:00Z",
        "config": {
            "confidence_threshold": 0.6,
            "model_id": "yolov8s_person"
        }
    }
]

MOCK_CONFIG = {
    "detection": {
        "confidence_threshold": 0.5,
        "nms_threshold": 0.4,
        "max_detections": 100,
        "model_id": "yolov8n_person",
        "enabled": True
    }
}

@router.get("/models")
async def get_available_models(category: str = None):
    """获取可用模型列表"""
    if category:
        return [model for model in MOCK_MODELS if model.get("category") == category]
    return MOCK_MODELS

@router.get("/categories")
async def get_algorithm_categories():
    """获取算法分类列表"""
    return [
        {
            "id": "crowd_safety",
            "name": "人流安全管理",
            "description": "人员检测、密度分析、异常聚集检测",
            "icon": "User",
            "color": "#f56565"
        },
        {
            "id": "behavior_safety",
            "name": "行为安全监测",
            "description": "姿态检测、冲突识别、危险行为预警",
            "icon": "Shield",
            "color": "#ed8936"
        },
        {
            "id": "hygiene_management",
            "name": "环境卫生管理",
            "description": "垃圾检测、清洁度评估、保洁效率分析",
            "icon": "Delete",
            "color": "#38a169"
        },
        {
            "id": "energy_management",
            "name": "能耗管理",
            "description": "照明检测、空间占用、节能优化",
            "icon": "Lightning",
            "color": "#3182ce"
        },
        {
            "id": "classroom_quality",
            "name": "课堂质量评估",
            "description": "专注度分析、参与度跟踪、学习效果评估",
            "icon": "Reading",
            "color": "#805ad5"
        },
        {
            "id": "teaching_quality",
            "name": "教学质量评估",
            "description": "教师行为分析、师生互动、教学风格评估",
            "icon": "Trophy",
            "color": "#d53f8c"
        }
    ]

@router.get("/models/{model_id}")
async def get_model_info(model_id: str):
    """获取模型信息"""
    for model in MOCK_MODELS:
        if model["model_id"] == model_id:
            return model
    raise HTTPException(status_code=404, detail="Model not found")

@router.get("/detection/tasks")
async def get_detection_tasks(camera_id: Optional[str] = None):
    """获取检测任务列表"""
    tasks = MOCK_TASKS
    if camera_id:
        tasks = [task for task in tasks if task["camera_id"] == camera_id]
    return tasks

@router.post("/detection/tasks")
async def create_detection_task(
    camera_id: str,
    task_type: str = "person_detection",
    config: Optional[Dict[str, Any]] = None
):
    """创建检测任务"""
    new_task = {
        "task_id": f"task_{len(MOCK_TASKS) + 1:03d}",
        "camera_id": camera_id,
        "task_type": task_type,
        "status": "pending",
        "created_at": datetime.now().isoformat() + "Z",
        "updated_at": datetime.now().isoformat() + "Z",
        "config": config or {}
    }
    MOCK_TASKS.append(new_task)
    return new_task

@router.get("/config/detection")
async def get_detection_config():
    """获取检测配置"""
    return MOCK_CONFIG["detection"]

@router.put("/config/detection")
async def update_detection_config(config: Dict[str, Any]):
    """更新检测配置"""
    MOCK_CONFIG["detection"].update(config)
    return MOCK_CONFIG["detection"]

@router.post("/models")
async def create_algorithm_model(model_data: Dict[str, Any]):
    """创建新的算法模型"""
    import uuid
    from datetime import datetime
    
    new_model = {
        "model_id": model_data.get("model_id", f"model_{uuid.uuid4().hex[:8]}"),
        "name": model_data.get("name", "新算法模型"),
        "version": model_data.get("version", "1.0.0"),
        "type": model_data.get("type", "custom"),
        "category": model_data.get("category", "custom"),
        "status": "inactive",  # 新创建的模型默认为非激活状态
        "accuracy": model_data.get("accuracy", 0.0),
        "performance_metrics": model_data.get("performance_metrics", {
            "fps": 0,
            "latency": 0,
            "memory_usage": 0
        }),
        "description": model_data.get("description", ""),
        "created_at": datetime.now().isoformat() + "Z",
        "updated_at": datetime.now().isoformat() + "Z"
    }
    
    MOCK_MODELS.append(new_model)
    return new_model

@router.put("/models/{model_id}")
async def update_algorithm_model(model_id: str, model_data: Dict[str, Any]):
    """更新算法模型"""
    from datetime import datetime
    
    for i, model in enumerate(MOCK_MODELS):
        if model["model_id"] == model_id:
            # 更新模型数据
            MOCK_MODELS[i].update(model_data)
            MOCK_MODELS[i]["updated_at"] = datetime.now().isoformat() + "Z"
            return MOCK_MODELS[i]
    
    raise HTTPException(status_code=404, detail="Model not found")

@router.delete("/models/{model_id}")
async def delete_algorithm_model(model_id: str):
    """删除算法模型"""
    for i, model in enumerate(MOCK_MODELS):
        if model["model_id"] == model_id:
            deleted_model = MOCK_MODELS.pop(i)
            return {"message": "Model deleted successfully", "model": deleted_model}
    
    raise HTTPException(status_code=404, detail="Model not found")

@router.post("/models/{model_id}/toggle")
async def toggle_algorithm_model(model_id: str):
    """切换算法模型的激活状态"""
    from datetime import datetime
    
    for model in MOCK_MODELS:
        if model["model_id"] == model_id:
            model["status"] = "active" if model["status"] == "inactive" else "inactive"
            model["updated_at"] = datetime.now().isoformat() + "Z"
            return model
    
    raise HTTPException(status_code=404, detail="Model not found")

# 摄像头算法绑定相关的mock数据
CAMERA_ALGORITHM_BINDINGS = [
    {
        "binding_id": "binding_001",
        "camera_id": "camera_001",
        "camera_name": "教学楼A-101教室",
        "algorithms": [
            {
                "model_id": "attention_analyzer",
                "enabled": True,
                "priority": 1,
                "config": {
                    "confidence_threshold": 0.7,
                    "detection_interval": 5
                }
            },
            {
                "model_id": "participation_tracker",
                "enabled": True,
                "priority": 2,
                "config": {
                    "confidence_threshold": 0.6,
                    "detection_interval": 10
                }
            }
        ],
        "created_at": "2024-01-01T00:00:00Z",
        "updated_at": "2024-01-01T00:00:00Z"
    },
    {
        "binding_id": "binding_002",
        "camera_id": "camera_002",
        "camera_name": "食堂入口",
        "algorithms": [
            {
                "model_id": "yolov8n_person",
                "enabled": True,
                "priority": 1,
                "config": {
                    "confidence_threshold": 0.5,
                    "detection_interval": 1
                }
            },
            {
                "model_id": "crowd_density_analyzer",
                "enabled": True,
                "priority": 2,
                "config": {
                    "confidence_threshold": 0.8,
                    "detection_interval": 3
                }
            }
        ],
        "created_at": "2024-01-01T00:00:00Z",
        "updated_at": "2024-01-01T00:00:00Z"
    }
]

@router.get("/camera-bindings")
async def get_camera_algorithm_bindings(camera_id: str = None):
    """获取摄像头算法绑定列表"""
    if camera_id:
        return [binding for binding in CAMERA_ALGORITHM_BINDINGS if binding["camera_id"] == camera_id]
    return CAMERA_ALGORITHM_BINDINGS

@router.get("/camera-bindings/{camera_id}")
async def get_camera_binding(camera_id: str):
    """获取指定摄像头的算法绑定"""
    for binding in CAMERA_ALGORITHM_BINDINGS:
        if binding["camera_id"] == camera_id:
            return binding
    raise HTTPException(status_code=404, detail="Camera binding not found")

@router.post("/camera-bindings")
async def create_camera_binding(binding_data: Dict[str, Any]):
    """创建摄像头算法绑定"""
    import uuid
    from datetime import datetime
    
    new_binding = {
        "binding_id": f"binding_{uuid.uuid4().hex[:8]}",
        "camera_id": binding_data.get("camera_id"),
        "camera_name": binding_data.get("camera_name", ""),
        "algorithms": binding_data.get("algorithms", []),
        "created_at": datetime.now().isoformat() + "Z",
        "updated_at": datetime.now().isoformat() + "Z"
    }
    
    CAMERA_ALGORITHM_BINDINGS.append(new_binding)
    return new_binding

@router.put("/camera-bindings/{camera_id}")
async def update_camera_binding(camera_id: str, binding_data: Dict[str, Any]):
    """更新摄像头算法绑定"""
    from datetime import datetime
    
    for i, binding in enumerate(CAMERA_ALGORITHM_BINDINGS):
        if binding["camera_id"] == camera_id:
            CAMERA_ALGORITHM_BINDINGS[i].update(binding_data)
            CAMERA_ALGORITHM_BINDINGS[i]["updated_at"] = datetime.now().isoformat() + "Z"
            return CAMERA_ALGORITHM_BINDINGS[i]
    
    raise HTTPException(status_code=404, detail="Camera binding not found")

@router.delete("/camera-bindings/{camera_id}")
async def delete_camera_binding(camera_id: str):
    """删除摄像头算法绑定"""
    for i, binding in enumerate(CAMERA_ALGORITHM_BINDINGS):
        if binding["camera_id"] == camera_id:
            deleted_binding = CAMERA_ALGORITHM_BINDINGS.pop(i)
            return {"message": "Camera binding deleted successfully", "binding": deleted_binding}
    
    raise HTTPException(status_code=404, detail="Camera binding not found")